Laser radars are widely used in systems such as an autopilot control system. Laser point cloud data in a laser point cloud acquired by the laser radar can be used for establishing a model for environment perception. Currently, the acquisition time of a laser point needs to be represented by using a timestamp with a relatively high precision, for example, a timestamp with 64-bit precision, to meet the data synchronization requirement, for example, the requirement of synchronizing data when different data receivers cooperate to create a three-dimensional model of a vehicle traveling environment by using the laser point cloud data. Currently, a commonly employed solution is to save, in laser point data of each laser point in a laser point cloud, a timestamp with 64-bit precision for representing an acquisition time of the each laser point.
However, there are massive laser point cloud data. On one hand, it takes a relatively long time for the CPU or GPU to process 64-bit precision timestamps of the laser point data, which cannot meet an extremely high real-time requirement of operations in the autopilot control system, affecting the stability and safety of the autopilot control system. On the other hand, when the laser point cloud data needs to be saved to perform offline debugging and in other similar cases, it consumes much storage resources to store the laser point cloud data with 64-bit precision timestamps.